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Hoseini, Seyed Mehdi. (1399). Persian Speech Emotion Recognition Approach based on Multilayer Perceptron. سامانه مدیریت نشریات علمی, 2(3), 177-187. doi: 10.22054/dcm.2021.13682
Seyed Mehdi Hoseini. "Persian Speech Emotion Recognition Approach based on Multilayer Perceptron". سامانه مدیریت نشریات علمی, 2, 3, 1399, 177-187. doi: 10.22054/dcm.2021.13682
Hoseini, Seyed Mehdi. (1399). 'Persian Speech Emotion Recognition Approach based on Multilayer Perceptron', سامانه مدیریت نشریات علمی, 2(3), pp. 177-187. doi: 10.22054/dcm.2021.13682
Hoseini, Seyed Mehdi. Persian Speech Emotion Recognition Approach based on Multilayer Perceptron. سامانه مدیریت نشریات علمی, 1399; 2(3): 177-187. doi: 10.22054/dcm.2021.13682

Persian Speech Emotion Recognition Approach based on Multilayer Perceptron

International Journal of Digital Content Management
مقاله 10، دوره 2، شماره 3، خرداد 2021، صفحه 177-187    اصل مقاله (465.93 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22054/dcm.2021.13682
نویسنده
Seyed Mehdi Hoseini*
Department of Computer Science, University of Mazandaran, Babolsar, Iran.
چکیده
Emotion recognition from speech has noticeable applications within the speech-processing systems. The goal of this paper is to permit a totally natural interaction among human and system. In this paper, an attempt is made to design and implement a system to determine and detect emotions of anger and happiness in the Persian speech signals. Research on recognizing some emotions has been done in most languages, but due to the difficulty of creating a speech database, so far little research has been done to identify emotions in Persian speech. In this article, because of the dearth of a suitable database in Persian to detect feelings, before everything, a database for moods of happiness and anger and neutral (with no emotion) in Persian, including 720 sentences was set up. Then the frequency features of speech signals obtained from Fourier transform such as maximum, minimum, median and mean as well as LPC coefficients were extracted. Then, the MLP neural network was used to detect emotions of happiness and anger. Results show that our algorithm performs 87.74% accurately.
کلیدواژه‌ها
Emotion Recognition؛ Speech Processing؛ LPC Coefficients؛ Neural Network
آمار
تعداد مشاهده مقاله: 175
تعداد دریافت فایل اصل مقاله: 328


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